)]}'
{
  "commit": "64764fd408d1dc0d7c50324d1fa44b33277ee494",
  "tree": "476c87bcea6acc3ab8085897f4d9602b3d973f88",
  "parents": [
    "9b6bb651da938a0561377398f0b859159c65a6cc"
  ],
  "author": {
    "name": "Urvang Joshi",
    "email": "urvang@google.com",
    "time": "Wed Feb 28 11:32:25 2024 -0800"
  },
  "committer": {
    "name": "Urvang Joshi",
    "email": "urvang@google.com",
    "time": "Mon Mar 04 08:05:13 2024 -0800"
  },
  "message": "Guided CNN Restoration: completely rewrite the code.\n\nEarlier, for each CNN model:\n- Encoder was running 4 inferences: 1 for full frame used by NONE\n  quadtree partition and 3 for partial frame (multiple of quadtree unit\n  size) used by SPLIT, VERT and HORZ quadtree partitions.\n- Decoder was running 2 inferences: 1 for full frame and 1 for partial\n  frame.\n\nInstead, we rewrite the code so that:\n- Both encoder and decoder run just 1 inference each.\n- For each qudtree unit, we perform RDO to pick best of NONE, SPLIT,\n  HORZ and VERT partition types using RDCost.\n\nAlso, related improvements to code:\n- Clear demarkations for encoder and decoder related functions.\n- Refactor lots of repeated logic for inference, rate computation etc.\n- Pad width and height to allow 4 downscale/upscale, as done by current\n  models.\n- If there is an error in inference, report an error at codec level.\n",
  "tree_diff": [
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}